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United States Patent |
5,319,720
|
Yokoyama
,   et al.
|
June 7, 1994
|
Appearance inspecting method
Abstract
An appearance inspecting method includes the steps of dividing a recognized
image of an object into regions based on two attributes of image points
within the image calculating the minimum distance of each point within a
target region to a periphery of the target region, and measuring a size of
the target region from the maximum of the calculated minimum distances of
the points in the target region. A distance measuring method for measuring
a distance of each point within a region to a periphery of the region
includes the steps of dividing each region to be measured by lines made of
picture elements arranged in one direction, sequentially integrating the
distance of each point from the periphery of the region from its outer
side to a middle point in each line from one end line to the other end
line of the region to thereby obtain the minimum distance of each point,
and the distance of each point from the periphery of the region is
sequentially integrated from its outer side to the middle point in each
line from the other end line to the one end line of the region to thereby
obtain the minimum distance of each point.
Inventors:
|
Yokoyama; Haruhiko (Osaka, JP);
Nakao; Masaya (Kadoma, JP)
|
Assignee:
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Matsushita Electric Industrial Co., Ltd. (Osaka, JP)
|
Appl. No.:
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913190 |
Filed:
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July 14, 1992 |
Foreign Application Priority Data
Current U.S. Class: |
382/145; 382/165; 382/173; 382/199 |
Intern'l Class: |
G06K 009/00 |
Field of Search: |
382/22,28,8,9
358/106,107
|
References Cited
U.S. Patent Documents
4720869 | Jan., 1988 | Wadia | 382/22.
|
Primary Examiner: Boudreau; Leo H.
Attorney, Agent or Firm: Wenderoth, Lind & Ponack
Claims
What is claimed is:
1. A computer implemented object inspection method comprising the steps of:
viewing the object with a camera to obtain a colorized image of an image
frame including the object and digitizing the colonized image to obtain
color image data of image points within the image frame including the
object;
dividing the image frame including the object into different image regions
based on at least two color attributes represented by the color image cata
at the image points within the image frame, wherein at least one of said
image regions is a target image region of the object;
calculating a minimum distance from each image point contained in the
target image region of the object to a periphery of the target image
region of the object to obtain a plurality of minimum distances
respectively associated with the image points within the target image
region of the object;
determining a maximum distances from among the plurality of minimum
distance; and,
judging an acceptability of the object based on the maximum distance.
2. A computer implemented object inspection method as claimed in claim 1,
further comprising a smoothing process step, executed before said dividing
step, of determining a means value of localized image sections of the
image frame and generating the color image at according to the mean value
of the localized image sections.
3. A computer implemented object inspection method as claimed in claim 1,
further comprising a smoothing process step, executed before said dividing
sep, of determining a means value of localized image sections of the image
frame and generating the color image data according to the means value of
the localized image sections, and wherein said calculating step includes
(1) dividing the target image region by parallel lines each containing
image points arranged in one direction, the parallel lines including one
end line at one end of the target region and another end line at the other
end of the target region, (2) sequentially calculating, for each line in
succession from the one end line to the other end line, a distance for
each image point from ends of each line to a middle of each line to obtain
a minimum distance or each image point, and (3) sequentially calculating,
or each line in succession from the other end line to the one end line, a
distance for each image point from ends of each line to a middle of each
line to obtain a minimum distance for each image point.
4. A computer implemented object inspection method as claimed in claim 1,
wherein said calculating step includes (1) dividing the target region by
parallel lines each containing image points arrange in one direction, the
parallel lines including one end line at one end of the target region and
another end line at the other end of the target region, (2) sequentially
calculating, for each line in succession from the one end line to the
other end line, a distance for each image point from ends of each line to
a middle of each line to obtain a minimum distance for each image point,
and (3) sequentially calculating, for each line in succession rom the
other end line to the one end line, a distance for each image point from
ends of each line to a middle of each line to obtain a minimum distance
for each image point.
5. A computer implemented object inspection method as claimed in claim 1,
wherein said at least two color attributes includes two different hues.
6. A computer implemented object inspection method as claimed in claim 2,
wherein said at least two color attributes include two different hues.
7. A computer implemented object inspection method as claimed in claim 3,
wherein said at least two color attributes includes two different hues.
8. A computer implemented object inspection method as claimed in claim 4,
wherein said at least two color attributes include two different hues.
9. A computer implemented object inspection method as claimed in claim 1,
wherein said at least two color attributes includes two of a hue, a
saturation and a lightness.
10. A computer implemented object inspection method as claimed in claim 2,
wherein said at least two color attributes include two of a hue, a
saturation and a lightness.
11. A computer implemented object inspection method as claimed in claim 3,
wherein said at least two color attributes include two of a hue,
saturation and a lightness.
12. A computer implemented object inspection method as claimed in claim 4,
wherein said at least two color attributes include two of a hue, a
saturation and a lightness.
Description
BACKGROUND OF THE INVENTION
The present invention relates to an inspecting method for inspecting the
appearance, for example, processing irregularity of the surface of an
object such as electronic parts, e.g. capacitors and a distance measuring
method used for the inspecting method.
A specific example of a previously used appearance inspecting method will
be described.
A UF capacitor is a kind of chip capacitor, which is applied with an
ultraviolet curing resin on the surface thereof (referred to as an applied
surface) coming in contact with a substrate when mounted, for the purpose
of insulation and protection. If the resin is not applied sufficiently,
the capacitor is judged to have poor wetting. A region of the capacitor
where the resin is fully applied is called a wetting part, and a region
thereof where the resin is not applied enough is called a poor wetting
part. In the conventional example, the whole area of the poor wetting part
is calculated and it is assumed that when the size of the area of the poor
wetting part is within a predetermined allowable value, the capacitor is
decided to be acceptable. On the other hand, if the size exceeds the
predetermined allowable value, the capacitor is decided to be
unacceptable.
According to a conventional method, however, the acceptability of the
applied surface of the UF capacitor is determined based only by the size
of the whole area of the poor wetting part, and the resulting decision may
be different from the decision an operator would make. For example, in
FIGS. 11A and 11B wherein reference numerals 31 and 33 denote wetting
parts and 32 and 34 denote poor wetting parts, assuming the size of the
area of the poor wetting part 32 in FIG. 11A is larger than a
predetermined value it is decided that it is unacceptable and an operator
would judge similarly. On the other hand, assuming the size of the area of
the poor wetting part 34 in FIG. 11B is also larger than the predetermined
value, it is decided that it is unacceptable, but the decision of the
operator would acceptable because the actual poor wetting part is very
small at the periphery of the UF capacitor.
SUMMARY OF THE INVENTION
The object of the present invention is therefore to provide an appearance
inspecting method capable of shortening the processing time through high
speed operation and improving the accuracy of the acceptability decision
and also to provide a distance measuring method of a short processing time
used in the appearance inspecting method.
In accomplishing these and other objects, according to one aspect of the
present invention, there is provided an appearance inspecting method
comprising the steps of:
dividing a recognized image of an object into two colors attributes of
points within the image;
calculating the minimum distance of each point within a target region to a
periphery of in the target region; and
measuring a size of the target region from the maximum of the calculated
minimum distances of the points in the target region.
According to another aspect of the present invention, there is provided a
distance measuring method for measuring a distance of each point within a
region to a periphery of the region, which comprises the steps of:
dividing each region to be measured by lines made of picture elements
arranged in one direction; and
sequentially integrating the distance of each point from the periphery of
the region from its outer side to a middle point in each line from one end
line to the other end line of the region to thereby obtain the minimum
distance of each point, and the distance of each point from the periphery
of the region is sequentially integrated from its outer side to the middle
point in each line from the other end line to the one end line of the
region to thereby obtain the minimum distance of each point.
BRIEF DESCRIPTION OF THE DRAWINGS
These and other objects and features of the present invention will become
clear from the following description taken in conjunction with the
preferred embodiment thereof with reference to the accompanying drawings
in which:
FIG. 1 a flow chart of the overall process in an appearance inspecting
method according to one preferred embodiment of the present invention;
FIG. 2 is a diagram explanatory of a mask operator used in a smoothing
process;
FIG. 3 is a graph of the color distribution of a UF capacitor;
FIG. 4 is a diagram of codes of eight neighborhood points;
FIG. 5 is a diagram explanatory of the processing order when the distance
is calculated;
FIG. 6 is a diagram explanatory of a distance measuring method;
FIG. 7 is a diagram of an example of a poor wetting part;
FIG. 8 is a diagram of another example of a poor wetting part;
FIG. 9 is a diagram of an applied surface of a UF capacitor;
FIG. 10 is a graph of the color distribution of a UF capacitor in a
conventional example;
FIGS. 11A and 11B are diagrams of applied surfaces of UF capacitors for
explaining the conventional method; and
FIGS. 12A and 12B are diagrams of applied surfaces of UF capacitors for
explaining the embodiment.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT
Before the description of the present invention proceeds, it is to be noted
that like parts are designated by like reference numerals throughout the
accompanying drawings.
A preferred embodiment of the present invention will be discussed
hereinbelow with reference to FIGS. 1 through 8.
Referring to a flow chart of FIG. 1 showing the overall process of an
appearance inspecting method of the embodiment, image signals inputted via
a TV camera are smoothed in step #1 after being turned to a finite number
of digital image data by sampling (digitizing) and A/D conversion with use
of a computer. The digital image data is divided into regions on the basis
of the color information in step #2. The distance of a target region is
calculated in step #3 and the size of the region is measured in step #4.
Each process will be described below in detail.
Smoothing process
Smoothing is achieved by a convolution operation using a smoothing mask
operator shown in FIG. 2, i.e., by calculating the mean of local sections.
Supposing that the original image data is expressed by {P(i,
j).vertline.1.ltoreq.i.ltoreq.N, 1.ltoreq.j.ltoreq.M} wherein i, j are
axes of abscissa and ordinate, of the image and N, M are the size of the
image frame, the smoothed image data {Q(k,l.vertline.1.ltoreq.k.ltoreq.N,
1.ltoreq.l.ltoreq.M} is obtained according to a formula (3) below:
##EQU1##
wherein m is the size of the mask operator.
Dividing process into region
FIG. 3 is a graph of the color distribution of a UF capacitor, in which an
axis of abscissa is an R axis (R being the quantity of red components) and
an axis of ordinate is a G axis (G being the quantity of green components)
similar to FIG. 10. Reference numerals 9, 10, and 11 indicate respectively
the color distribution of a wetting part, the color distribution of a poor
wetting part, and the color distribution of the background part (unseen
area of the capacitor). As is clear from FIG. 3, each region of the color
distribution is clearly distinguished from one another due to the
smoothing process described above without being influenced by color
irregularity and/or density irregularity. A line 12 separates the wetting
part 9 and the poor wetting part 10 from the background part 11. A line 13
separates the wetting part 9 and the poor wetting part 10.
The lines 12 and 13 satisfy the following equations (4) and (5):
k.sub.1 * r+l.sub.1 *g=s.sub.1 (4)
k.sub.2 *r+l.sub.2 *g=s.sub.2 (5)
In the equations, "r" indicates the size of R components, and "g" indicates
the size of G components.
When R and G components of the image obtained in the smoothing process are
denoted by R and G, respectively, dividing of the image into regions is
carried out in a manner described hereinbelow.
1) A1=k.sub.1 * R+l.sub.1 * G is operated according to above equation (4).
2) A1 is turned into two digits by a threshold value s.sub.1, thereby to
obtain B1.
As a result, the target region of the capacitor is detected.
3) A2=k.sub.2 * R+l.sub.2 * G is operated according to above equation (5).
4) A2 is converted to two digits by a threshold value s.sub.2, thereby to
obtain B2.
5) A product of B1 and B2 is calculated.
Accordingly, the poor wetting part is detected.
Distance calculating process
An image distance determined for the poor wetting part obtained in the
region dividing process. The image distance referred to above is obtained
by calculating the distance of each point within the region from the
periphery. Although it is necessary to calculate the distance sequentially
from the shortest point from the periphery, the processing time is
lengthy, and therefore, the embodiment executes a high speed calculation,
which will be described with reference to FIGS. 4 to 6.
More specifically, FIG. 4 is a table of codes of eight neighborhood points
of a target point d. FIG. 5 shows the processing order when two objective
regions are present. FIG. 6 is explanatory of the calculating method of
the distance. The distance is calculated in accordance with process 1) to
3) hereinbelow.
1) Zero is loaded in every element in an array of the distance data.
2) The following treatment is conducted for each line of the poor wetting
part from the uppermost line to a lower one as indicated in FIG. 5. Each
line is made of an array of picture elements arranged in one direction.
(i) The distance data is sequentially calculated according to an equation
(6) below for a continuous part where the poor wetting part exists
continuously in a target line (referred to as a "run" hereinafter) from
the left end to a middle point of the line.
d=min(d.sub.2 +DS, d.sub.3 +DV, d.sub.4 +DS, d.sub.5 +DH) (6)
As shown in FIG. 6, "d" is the distance from the periphery of the target
point, "DH" is the distance between the target point and an adjacent
neighborhood point in the horizontal direction, "DV" is the distance
between the target point and an adjacent neighborhood point in the
vertical direction, and "DS" is the distance from the target point to an
adjacent neighborhood point in the slantwise direction. The points
indicated by oblique lines in FIG. 6 are already calculated. The minimum
value of the added distances of the above distances DH, DV, and DS to the
already calculated distances d.sub.2, d.sub.3, d.sub.4, and d.sub.5 is the
distance of d.
(ii) Similarly, the distance data from the right end to the middle point of
the run of the poor wetting part in the target line is calculated in
accordance with an equation (7) below.
d=min(d.sub.1 +DH, d.sub.2 +DS, d.sub.3 +DV, d.sub.4 +DS) (7)
In the case where a plurality of runs are present, (i) and (ii) are carried
out for every run.
3) The poor wetting part is then processed as described hereinbelow
sequentially from the lowest line to an upper one.
(iii) The distance data is calculated from the left end to the middle point
of the run of the poor wetting part in the target line according to an
equation (8).
d=min(d, d.sub.5 +DH, d.sub.6 +DS, d.sub.7 +DV, d.sub.8 +DS)(8)
(iv) The distance data from the right end to the middle point of the run of
the poor wetting part in the target line is calculated according to an
equation (9).
d=min(d, d.sub.6 +DS, d.sub.7 +DV, d.sub.8 +DS, d.sub.1 +DH)(9)
If there are a plurality of runs, process (iii) and (iv) are performed for
every run.
In the present embodiment, it is assumed that when the radius of the
largest inscribed circle of the poor wetting part is within a
predetermined allowable value, the capacitor is decided to be acceptable.
On the other hand, if the radius exceeds the predetermined allowable
value, the capacitor is decided to be unacceptable.
The above method is much simplified as compared with a conventional method,
and as will be explained hereinafter that the above method ensures a
correct result.
FIG. 7 is an example of a pattern of the poor wetting part, in which
reference numerals 14 and 15 indicate a target point and a series of
middle points of runs, and a part indicated by oblique lines 16 is a set
of points used to determine the distance of the target point 14 when the
procedure up to the above process 2) is finished. A part of oblique lines
17 is a set of points related to the determination of the distance of the
target point 14 before the process 3) is completed after the process 2). A
white area 18 is a set of points not related to the distance determination
of the target point 14. FIG. 8 shows another example of a pattern of the
poor wetting part, intended to clarify that the distance can be measured
correctly even when a recessed part is present in the vicinity of a target
point.
It is apparent from FIG. 7 that the white part 18 occupies only a small
area. Therefore, the points inside the area 18 are not required for
determination of the distance. For instance, even when the distance of the
target point is short because of the presence of the recessed part in the
vicinity of the target point as shown in FIG. 8, the distance can be
calculated from a bottom point 19 of the recessed part.
Measuring process of size of region
The size of a region is determined by the maximum value of the distances
calculated in the distance calculating process.
The above-described method makes it possible to reduce the processing time
remarkably. The processing time of the data referred to earlier with
respect to the following reference method will be calculated in the
present invention as follows.
Time to divide the wetting part, poor wetting part, and background into
regions
T1=33msec (to smooth R components of the image)
T2=33msec (to smooth G components of the image)
T3=33msec (to calculate A1=k.sub.1 * R+l.sub.1 * G)
T4=33msec (to convert A1 to two digits (to obtain B1))
T5=33msec (to calculate A2=k.sub.2 * R+l.sub.2 * G)
T6=33msec (to convert A2 to two digits (to obtain B2))
T7=33msec (to calculate a product of B1 and B2)
Time to calculate the distance
T8=5000.times.8(D).times.10(S).times.0.5.mu.sec=200msec
It is to be noted that D=5000.times.8 in the calculation of T8 means "(the
number of points in the poor wetting part).times.(the number of
neighborhood points referred to for every point)".
Accordingly, the processing time T according to the present embodiment is:
T=T1+T2+T3+T4+T5+T6+T7+T8=431msec
As compared with the above process, the reference process will be described
below with reference to FIGS. 9 and 10.
FIG. 9 shows the applied surface of a UF capacitor, in which reference
numerals respectively indicate: 1 the configuration of the UF capacitor; 2
a poor wetting part of the capacitor; 3 the largest inscribed circle of
the poor wetting part 2; and 4 the radius of the largest inscribed circle
3.
FIG. 10 is a graph of the color distribution of the UF capacitor. An axis
of abscissa is an R axis (R being the quantity of ref components) and an
axis of ordinate is a G axis (G being the quantity of green components).
It is to be noted here that the above color distribution is obtained by
plotting each quantity of red components and green components at each
point of the regions on the axis of abscissa and axis of ordinate. In FIG.
10, reference numerals 5, 6, 7, and 8 are respectively the color
distribution in the wetting part, the color distribution of the poor
wetting part 2, the color distribution of the background (unseen area of
the capacitor), and a line separating the color distribution 6 of the poor
wetting part from the color distributions 5 and 7 of the other parts in a
manner the the color distribution 6 is least overlapped with the color
distributions 5 and 7.
Conventionally, an image of an object to be inspected is input via a TV
camera, subjected to sampling (digitizing) and A/D conversion, and finally
processed as a finite number of digital data by a computer.
The poor wetting part is divided into regions with use of the line 8 of
FIG. 10. The shortest distance of each point to the periphery of the
divided region of the poor wetting part is obtained. The maximum value of
the shortest distances, i.e., the radius of the largest inscribed circle
is defected.
For example, supporting that the size of the image data is 500.times.500,
the poor wetting part is a 50.times.100 rectangle, and the computer
processes at 2MIPS speed (0.5.mu.sec for one processing instruction), the
processing time will be roughly estimated as follows.
When the number of data to be processed is "D" and the number of
instructions for processing one data is "S", the processing time is
calculated according to a formula (D.times.S.times.0.5.mu.sec). Therefore,
the equations (1) and (2) below are held.
Time to divide the wetting part, poor wetting part, background part into
regions
T1=250000(D).times.20(S).times.0.5.mu.sec=2.5sec (1)
Time to calculate the distance
T2=5000.times.130(D).times.10(S).times.0.5.mu.sec=3.25sec (2)
D=5000.times.130 in the equation (2) stands for "(the number of points in
the poor wetting part).times.(the average of areas of inscribed circles of
the points)".
Accordingly, the total processing time T becomes:
T=T1+T2=5.75sec
Therefore, the processing time of the embodiment is 1/13 the time required
in the reference method, i.e., 1/13 of 5.75sec.
According to the embodiment, an image of an object is divided into regions
based on two color components of hue, the minimum distance of each point
from its periphery within a target region is calculated, and a size of the
target region from the maximum of the calculated minimum distances of the
points in the target region is measured. Therefore, the size of the poor
wetting part can be determined by the maximum value of the distances
calculated, and thus the processing time therefor can be greatly reduced.
For example, in FIGS. 12A and 12B wherein reference numerals 35 and 37
denote wetting parts and 36 and 38 denote poor wetting part, when the
radius of the inscribed circle of the poor wetting part in FIG. 12A is
larger than a predetermined value, it is decided that it is unacceptable,
and when the radius of the poor wetting part in FIG. 12B is smaller than
the predetermined value, it is decided that it is acceptable. This
decision of the acceptance is the same as the operator's decision, and
thus the accuracy of the decision can be improved.
As is described hereinabove, even if the brightness of an image of an
object is not uniform because of the surface roughness or the like, the
size of an unacceptable processing part of the object can be correctly
measured by the appearance inspecting method of the present embodiment and
the processing time therefor can be greatly reduced because of the
combination of the smoothing process and the dividing process based on the
color information.
According to the reference method, both the wetting part and the poor
wetting part have irregularity in color and density which result from the
various reflecting states at each point of the parts due to the surface
roughness, painting irregularity or noises of electric signals. Therefore,
if the wetting part and the poor wetting part are divided into regions by
the line 8 of FIG. 10, many points may be decided as included in the poor
wetting part although they should be decided to be inside the wetting
part. The reverse may be also true in many cases that the points which
should be detected to be inside the poor wetting part are decided to be
included in the wetting part. As a result, the radius of the largest
inscribed circle may be measured erroneously. Contrary to the above
reference method, in the embodiment, as is clear from FIG. 3, each region
of the color distribution can be clearly distinguished from one another
due to the smoothing process without being influenced by the color
irregularity and/or density irregularity.
Moreover, the distance of each point from the periphery in any region can
be calculated in a short processing time according to the distance
measuring method of the present embodiment.
In the dividing step, instead of using two different hue components as the
two color attributes, two of a single hue component saturation, and
lightness may be used. For example, a graph of distribution of a UF
capacitor may be made for dividing the wetting part, poor wetting part,
and background part into regions, in which an axis of abscissa is a
saturation and an axis of ordinate is a lightness.
Although the present invention has been fully described in connection with
the preferred embodiments thereof with reference to the accompanying
drawings, it is to be noted that various changes and modifications are
apparent to those skilled in the art. Such changes and modifications are
to be understood as included within the scope of the present invention as
defined by the appended claims unless they depart therefrom.
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